pfa {Correlplot}

Principal factor analysis

Description

Program pfa performs (iterative) principal factor analysis, which is based on the computation of eigenvalues of the reduced correlation matrix.

Usage

pfa(X, option = "data", m = 2, initial.communality = "R2", crit = 0.001, verbose = FALSE)

Arguments

X

A data matrix or correlation matrix

option

Specifies the type of matrix supplied by argument X. Values for option are data, cor or cov. data is the default.

m

The number of factors to extract (2 by default)

initial.communality

Method for computing initial communalites. Possibilities are R2 or maxcor.

crit

The criterion for convergence. The default is 0.001. A smaller value will require more iterations before convergence is reached.

verbose

When set to TRUE, additional numerical output is shown.

Value

Res

Matrix of residuals

Psi

Diagonal matrix with specific variances

La

Matrix of loadings

Shat

Estimated correlation matrix

Fs

Factor scores

Author(s)

Jan Graffelman ([e-mail])

References

Mardia, K.V., Kent, J.T. and Bibby, J.M. (1979) Multivariate analysis.

Rencher, A.C. (1995) Methods of multivriate analysis.

Satorra, A. and Neudecker, H. (1998) Least-Squares Approximation of off-Diagonal Elements of a Variance Matrix in the Context of Factor Analysis. Econometric Theory 14(1) pp. 156–157.

See Also

princomp

Examples

   X <- matrix(rnorm(100),ncol=2)
   out.pfa <- pfa(X)
#  based on a correlation matrix
   R <- cor(X)
   out.pfa <- pfa(R,option="cor")